SCIENCE@home

Land Cover and Crop Type Classification along the Season Based on Biophysical Variables Retrieved from Multi-Sensor High-Resolution Time Series

F. Waldner,M. Lambert,Wenjuan（李文娟） Li,M. Weiss,V. Demarez,David Morin,C. Marais-Sicre,O. Hagolle,F. Baret,P. Defourny

Published 2015 in Remote Sensing

ABSTRACT

With the ever-increasing number of satellites and the availability of data free of charge, the integration of multi-sensor images in coherent time series offers new opportunities for land cover and crop type classification. This article investigates the potential of structural biophysical variables as common parameters to consistently combine multi-sensor time series and to exploit them for land/crop cover classification. Artificial neural networks were trained based on a radiative transfer model in order to retrieve high resolution LAI, FAPAR and FCOVER from Landsat-8 and SPOT-4. The correlation coefficients between field measurements and the retrieved biophysical variables were 0.83, 0.85 and 0.79 for LAI, FAPAR and FCOVER, respectively. The retrieved biophysical variables’ time series displayed consistent average temporal trajectories, even though the class variability and signal-to-noise ratio increased compared to NDVI. Six random forest classifiers were trained and applied along the season with different inputs: spectral bands, NDVI, as well as FAPAR, LAI and FCOVER, separately and jointly. Classifications with structural biophysical variables reached end-of-season overall accuracies ranging from 73%–76% when used alone and 77% when used jointly. This corresponds to 90% and 95% of the accuracy level achieved with the spectral bands and NDVI. FCOVER appears to be the most promising biophysical variable for classification. When assuming that the cropland extent is known, crop type classification reaches 89% with spectral information, 87% with the NDVI and 81%–84% with biophysical variables.

PUBLICATION RECORD

  • Publication year

    2015

  • Venue

    Remote Sensing

  • Publication date

    2015-08-13

  • Fields of study

    Agricultural and Food Sciences, Geology, Computer Science, Environmental Science

  • Identifiers
    DOI 10.3390/RS70810400
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-76 of 76 references · Page 1 of 1

CITED BY

Showing 1-57 of 57 citing papers · Page 1 of 1